![](bg2.png)
bandwidth negotiation, and in particular during the
interval that lasts from the moment the ONU sends
the bandwidth requirement until the moment it can
start sending the buffered data. More data will arrive
at the ONU buffer that time and will remain in the
buffer until the next cycle. As a result these will not
be taken into account when the bandwidth request
was sent. OLT-based traffic prediction relies on
estimating future "on-average" bandwidth
requirements for all ONUs in the network based on
their previous bandwidth requests. A key drawback
of OLT-based prediction, however, is that it may not
accurately identify, and therefore respond, to rapid
changes in the ONU traffic. On the other hand
ONU-based prediction, can be performed within a
single cycle, since ONUs are able to constantly
monitor incoming traffic, and therefore can adapt to
traffic changes significantly faster.
Predictive techniques establish a mathematical
model that processes the series of data packets in
order to estimate the future traffic flow. A large
variety of traffic prediction algorithms for EPONs
have been proposed in the last years, in order to
improve the bandwidth allocation strategy and the
total system performance (Mcgarry et al., 2008;
Sadek and Khotanzad, 2004). These prediction
techniques can be executed at the side of OLT
(Hwand et al., 2008; Hwang et al., 2012)
or the
ONUs (Luo and Ansari, 2005; Swades et al., 2010;
Morato et al., 2001; Chan et al., 2009)
with the pros
and cons of each approach that have been described
earlier. The technique proposed in (Hwang et al.,
2008) consists of a two-stage bandwidth request
scheme. In the first stage, DBA is performed for the
next cycle at the ONU level assigning bandwidth to
the ONUs that have more unstable (difficult to
predict) traffic. In this way it becomes easier to
reduce the prediction error by shortening their
waiting times. In the next stage, a linear prediction-
based excess bandwidth request is done for the more
stable ONUs. At the OLT, the proportionally
available bandwidth for an ONU is allocated to
related traffic classes, strictly based on their
respective requests ordered by their priority. In
(Hwang et al., 2008), the authors propose a
prediction process that is based on genetic
expression programming to reduce the queue size
variation and the packet delay. Taking a different
approach (Luo and Ansari, 2005; Swades et al.,
2010; Morato et al., 2001; Chan et al., 2009) propose
prediction techniques that are applied at the ONUs.
In
(Luo and Ansari, 2005), a limited sharing with
traffic prediction scheme was proposed and shown
to enhance DBA process. For ONU-based traffic
prediction another approach was presented in
(Swades et al., 2010) where authors propose a linear
class-based prediction model that tries to estimate
the incoming traffic until the next polling cycle. This
model uses information from previous bandwidth
requests in order to predict bandwidth request at
each ONU in the network, according to the OLT
priority classes. The effect of long-range dependence
of internet traffic in the prediction was studied in
(Morato et al., 2001). In (Chan et al., 2009)
authors
propose a different approach in the EPON by
applying a remote repeater node (RN). The RN
provides electrical regeneration in order to increase
the total number of supported ONU’s and the reach
of the EPON. The proposed scheme processes the
incoming frames in order to improve performance of
downstream and upstream transmissions. While the
prior works have used complicated prediction
techniques at the ONUs, the estimates they produce
refer to a single parameter, that is the bandwidth to
be allocated, which is however a complex metric
(ratio of data size over time duration).
Within the context of ONU-based traffic
prediction, we propose a novel algorithm for
decreasing latency in EPONs. Our algorithm (a)
approximates the frame arrivals within the duration
of a single EPON cycle using least-mean-square
polynomials and (b) estimates the duration of the
upcoming cycle via a least-means-squares adaptive
filter. Subsequently, the two quantities are combined
to produce the amount of data that the ONU will
have accumulated by the time the next bandwidth
assignment from the OLT (GATE message) arrives.
The ONU then communicates the predicted rather
than the actual data to the OLT in the REPORT
message), thus providing the DBA mechanism with
a more informed guess of its traffic requirements.
We show via simulation that the incorporation of the
proposed prediction methods in the EPON operation
can reduce the frame delay from 25% up to 30%
when compared to the standard operation of the
limited and gated versions of Interleaved Polling
with Adaptive Cycle Time (IPACT), depending on
the traffic load and the burstiness of the incoming
traffic. Moreover, this significant performance
benefit is obtained by applying the prediction
algorithms locally at the ONUs and without any
further modification on the Multi-Point Control
Protocol
(MPCP) procedures or the operation of
IPACT. At the same time the proposed solution
exhibits a low computational complexity, which is a
particularly appealing feature when considering the
ONU processing capabilities and associated cost.
The rest of the paper is structured as follows:
OPTICS2014-InternationalConferenceonOpticalCommunicationSystems
16